knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" ) options(width = 120)
The goal of bench is to benchmark code, tracking execution time, memory allocations and garbage collections.
You can install the release version from CRAN with:
install.packages("bench")
Or you can install the development version from GitHub with:
# install.packages("pak") pak::pak("r-lib/bench")
bench::mark()
is used to benchmark one or a series of expressions, we feel it has a number of advantages over alternatives.
bench::press()
, which allows you to easily perform and combine benchmarks across a large grid of values.The times and memory usage are returned as custom objects which have human readable formatting for display (e.g. 104ns
) and comparisons (e.g. x$mem_alloc > "10MB"
).
There is also full support for plotting with ggplot2 including custom scales and formatting.
bench::mark()
Benchmarks can be run with bench::mark()
, which takes one or more expressions to benchmark against each other.
library(bench) set.seed(42) dat <- data.frame( x = runif(10000, 1, 1000), y = runif(10000, 1, 1000) )
bench::mark()
will throw an error if the results are not equivalent, so you don't accidentally benchmark inequivalent code.
bench::mark( dat[dat$x > 500, ], dat[which(dat$x > 499), ], subset(dat, x > 500) )
Results are easy to interpret, with human readable units.
bnch <- bench::mark( dat[dat$x > 500, ], dat[which(dat$x > 500), ], subset(dat, x > 500) ) bnch
By default the summary uses absolute measures, however relative results can be obtained by using relative = TRUE
in your call to bench::mark()
or calling summary(relative = TRUE)
on the results.
summary(bnch, relative = TRUE)
bench::press()
bench::press()
is used to run benchmarks against a grid of parameters.
Provide setup and benchmarking code as a single unnamed argument then define sets of values as named arguments.
The full combination of values will be expanded and the benchmarks are then pressed together in the result.
This allows you to benchmark a set of expressions across a wide variety of input sizes, perform replications and other useful tasks.
set.seed(42) create_df <- function(rows, cols) { out <- replicate(cols, runif(rows, 1, 100), simplify = FALSE) out <- setNames(out, rep_len(c("x", letters), cols)) as.data.frame(out) } results <- bench::press( rows = c(1000, 10000), cols = c(2, 10), { dat <- create_df(rows, cols) bench::mark( min_iterations = 100, bracket = dat[dat$x > 500, ], which = dat[which(dat$x > 500), ], subset = subset(dat, x > 500) ) } ) results
ggplot2::autoplot()
can be used to generate an informative default plot.
This plot is colored by gc level (0, 1, or 2) and faceted by parameters (if any).
By default it generates a beeswarm plot, however you can also specify other plot types (jitter
, ridge
, boxplot
, violin
).
See ?autoplot.bench_mark
for full details.
ggplot2::autoplot(results)
You can also produce fully custom plots by un-nesting the results and working with the data directly.
library(tidyverse) results %>% unnest(c(time, gc)) %>% filter(gc == "none") %>% mutate(expression = as.character(expression)) %>% ggplot(aes(x = mem_alloc, y = time, color = expression)) + geom_point() + scale_color_bench_expr(scales::brewer_pal(type = "qual", palette = 3))
system_time()
bench also includes system_time()
, a higher precision alternative to system.time().
bench::system_time({ i <- 1 while(i < 1e7) { i <- i + 1 } }) bench::system_time(Sys.sleep(.5))
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